Saturday, 26 April 2008

There are my notes from the data burst 'Maritime Memorials, visualised' by Fiona Romeo, at the MCG Spring meeting. There's some background to my notes about the conference in a previous post. Any of my comments are in [square brackets] below.

This was a quick case study: could they use information visualisation to make more of collections datasets? [The site discussed isn't live yet, but should be soon]

A common visualisation method is maps. It's a more visual way for people to look at the data, it brings in new stories, and it helps people get sense of the terrain in e.g. expeditions. They exported data directly from MultiMimsy XG and put it into KML templates.

Another common method is timelines. If you have well-structured data you could combine the approaches e.g. plotting stuff on map and on a timeline.

Onto the case study: they had a set of data about memorials around the UK/world. It was quite rich content and they felt that a catalogue was probably not the best way to display it.

They commissioned Stamen Design. They sent CSV files for each table in the database, and no further documentation. [Though since it's MultiMimsy XG I assume they might have sent the views Willo provide rather than the underlying tables which are a little more opaque.]

Slide 4 lists some reasons arguments for trying visualisations, including the ability to be beautiful and engaging, provocative rather than conclusive, appeal to different learning styles and to be more user-centric (more relevant).

'Mine the implicit data' to find meaningful patterns and representations - play with the transcripts of memorial texts to discover which words or phrases occur frequently.

'Find the primary objects and link them' - in this case it was the text of the memorials, then you could connect the memorials through the words they share.

The 'maritime explorer' will let you start with a word or phrase and follow it through different memorials.

Most interesting thing about the project is the outcome - not only new outputs (the explorer, KML, API), but also a better understanding of their data (geocoded, popular phrases, new connections between transcripts), and the idea that CSV files are probably good enough if you want to release your data for creative re-use.

Approaches to metadata enhancement might include curation, the application of standards, machine-markup (e.g. OpenCalais), social tagging or the treatment of data by artisans. This was only a short (2 - 3 weeks) project but the results are worth it.

[I can't wait to try the finished 'explorer', and I loved the basic message - throw your data out there and see what comes back - you will almost definitely learn more about your data as well as opening up new ways in for new audiences.]